Networking- What are the unix commands - steps to do the following- -.pdf
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Networking: What are the unix commands / steps to do the following: - Redirect the output of the date command to a file named date . Overwrite the file if it exists. - Redirect the contents of the /etc/system-release file to the system file. Append to the file if it exists..
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#pragma once
#include <functional> // std::less
#include <iostream>
#include <queue> // std::queue
#include <utility> // std::pair
template <typename K, typename V, typename Comparator = std::less<K>>
class BinarySearchTree
{
public:
using key_type = K;
using value_type = V;
using key_compare = Comparator;
using pair = std::pair<key_type, value_type>;
using pointer = pair*;
using const_pointer = const pair*;
using reference = pair&;
using const_reference = const pair&;
using difference_type = ptrdiff_t;
using size_type = size_t;
private:
struct BinaryNode
{
pair element;
BinaryNode *left;
BinaryNode *right;
BinaryNode( const_reference theElement, BinaryNode *lt, BinaryNode *rt )
: element{ theElement }, left{ lt }, right{ rt } { }
BinaryNode( pair && theElement, BinaryNode *lt, BinaryNode *rt )
: element{ std::move( theElement ) }, left{ lt }, right{ rt } { }
};
using node = BinaryNode;
using node_ptr = node*;
using const_node_ptr = const node*;
node_ptr _root;
size_type _size;
key_compare comp;
public:
BinarySearchTree() {
// TODO
}
BinarySearchTree( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree( BinarySearchTree && rhs ) {
// TODO
}
~BinarySearchTree() {
// TODO
}
const_reference min() const { return min( _root )->element; }
const_reference max() const { return max( _root )->element; }
const_reference root() const {
// TODO
}
bool contains( const key_type & x ) const { return contains( x, _root ); }
value_type & find( const key_type & key ) { return find( key, _root )->element.second; }
const value_type & find( const key_type & key ) const { return find( key, _root )-
>element.second; }
bool empty() const {
// TODO
}
size_type size() const {
// TODO
}
void clear() {
clear( _root );
_size = 0;
}
void insert( const_reference x ) { insert( x, _root ); }
void insert( pair && x ) { insert( std::move( x ), _root ); }
void erase( const key_type & x ) { erase(x, _root); }
BinarySearchTree & operator=( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree & operator=( BinarySearchTree && rhs ) {
// TODO
}
private:
void insert( const_reference x, node_ptr & t ) {
// TODO
}
void insert( pair && x, node_ptr & t ) {
// TODO
}
void erase( const key_type & x, node_ptr & t ) {
// TODO
}
const_node_ptr min( const_node_ptr t ) const {
// TODO
}
const_node_ptr max( const_node_ptr t ) const {
// TODO
}
bool contains( const key_type & x, const_node_ptr t ) const {
// TODO
}
node_ptr find( const key_type & key, node_ptr t ) {
// TODO
}
const_node_ptr find( const key_type & key, const_node_ptr t ) const {
// TODO
}
void clear( node_ptr & t ) {
// TODO
}
node_ptr clone ( const_node_ptr t ) const {
// TODO
}
public:
template <typename KK, typename VV, typename CC>
friend void printLevelByLevel( const BinarySearchTree<KK, VV, CC>& bst, std::ostream & out
);
template <typename KK, typename VV, typename CC>
friend std::ostream& printNode(std::ostream& o, const typename BinarySearchTree<KK, VV,
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#Step 1: Import the required Python libraries:
import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
(X, y), (_, _) = keras.datasets.cifar10.load_data()
#Selecting a single class of images
#The number was randomly chosen and any number
#between 1 and 10 can be chosen
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#Defining the Input shape
image_shape = (32, 32, 3)
latent_dimensions = 100
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def build_generator():
model = Sequential()
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model.add(Dense(128 * 8 * 8, activation="relu",
input_dim=latent_dimensions))
model.add(Reshape((8, 8, 128)))
model.add(UpSampling2D())
model.add(Conv2D(128, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
model.add(Conv2D(64, kernel_size=3, padding="same"))
model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(Conv2D(3, kernel_size=3, padding="same"))
model.add(Activation("tanh"))
#Generating the output image
noise = Input(shape=(latent_dimensions,))
image = model(noise)
return Model(noise, image)
#Step 5: Define a utility function to build the discriminator.
def build_discriminator():
#Building the convolutional layers
#to classify whether an image is real or fake
model = Sequential()
model.add(Conv2D(32, kernel_size=3, strides=2,
input_shape=image_shape, padding="same"))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=3, strides=2, padding="same"))
model.add(ZeroPadding2D(padding=((0,1),(0,1))))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=3, strides=2, padding="same"))
model.add(BatchNormalization(momentum=0.82))
model.add(LeakyReLU(alpha=0.2))
model.add(Dropout(0.25))
model.add(Conv2D(256, kernel_size=3, strides=1, padding="same"))
model.add(BatchNormalization(momentum=0.8))
model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * genera.
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import math import pandas as pd def information_gain_target(dataset_file): # Input: dataset_file
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The code I have so far feels about 70% complete. (I plan to get rid of the qsort function and
implement my own merge-sort algo but for now, you can use it.) There are a few errors that
prevent it from running. Here is the code I have so far:
#include<stdio.h>
#include<stdlib.h>
#include<math.h>
#include<string.h>
#define MAX_N 500000
#define MAX_S 10
struct tugboat {
int x, y, dist;
char name[21];
};
int cmp(const void* a, const void* b) {
const struct tugboat* t1 = (const struct tugboat*) a;
const struct tugboat* t2 = (const struct tugboat*) b;
if (t1->dist != t2->dist) {
return t1->dist - t2->dist;
} else {
return strcmp(t1->name, t2->name);
}
}
int main() {
int xs, ys, xn, yn;
scanf("%d%d%d%d", &xs, &ys, &xn, &yn);
int n;
scanf("%d", &n);
struct tugboat tugboats[MAX_N];
int i;
for (i = 0; i < n; i++) {
scanf("%d%d%s", &tugboats[i].x, &tugboats[i].y, tugboats[i].name);
int dx = tugboats[i].x - xs;
int dy = tugboats[i].y - ys;
tugboats[i].dist = dx * dx + dy * dy;
}
qsort(tugboats, n, sizeof(struct tugboat), cmp);
int num_tugs = 0;
char tugs[MAX_N][21];
int prev_x = xs, prev_y = ys;
for (i = 0; i < n; i++) {
if (tugboats[i].x >= prev_x && tugboats[i].y >= prev_y) {
num_tugs++;
strcpy(tugs[num_tugs - 1], tugboats[i].name);
prev_x = tugboats[i].x;
prev_y = tugboats[i].y;
}
}
printf("%d\n", num_tugs);
for (i = 0; i < num_tugs; i++) {
printf("%s\n", tugs[i]);
}
return 0;
}
Here are the instructions:
Tugboat Troubles Objective Give practice with sorting in C. Story The ship's engine has ceased,
and now begins the preparations of taking the ship to the harbor for repairs. Luckily it's a straight
shot to the harbor. The bad news is that the journey is long. There are several tugboats currently
scattered throughout the ocean that can help move the ship to the harbor, but they may have to go
out of their way to assist, and taking your crippled ship the full distance is going to be taxing on
resources for any 1 particular tugboat. The tugboats have made an agreement. The tugboats will
take the shortest path to the direct route that your ship will travel. Once the tugboats are on the
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dangerous on the high seas, and you're afraid that your ship may f.
Need help! On June 1- Bash Salad Dressings creates a petty cash fund w.pdf
Need help!
On June 1, Bash Salad Dressings creates a petty cash fund with an imprest balance of $600 .
During June, Jackson Demers, the fund custodian, signs the following petty cash tickets: (Click
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custodian is responsible. plus the that support payments from the fund. The internal control
feature of an imprest fund is that it the amount of money for which the Data table Requirements
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Need help with question 6- Please and thank you! Kinesiology A(n) mu.pdf
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Kinesiology
A ( n ) muscle originates at the scapula and clavicle and inserts at the humerus. A ( n ) muscle
originates at the upper trunk and inserts at the humerus. extrinsic, intrinsic flexor, extensor
intrinsic, extrinsic extensor, flexor.
Need help with international accounting- please- thanks- 1- How is it.pdf
Need help with international accounting, please; thanks.
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be appropriate polymorphism: overloading, overriding methods, and dynamic binding 7)
Program should take advantage of the inheritance properties as appropriate.
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#pragma once
#include <functional> // std::less
#include <iostream>
#include <queue> // std::queue
#include <utility> // std::pair
template <typename K, typename V, typename Comparator = std::less<K>>
class BinarySearchTree
{
public:
using key_type = K;
using value_type = V;
using key_compare = Comparator;
using pair = std::pair<key_type, value_type>;
using pointer = pair*;
using const_pointer = const pair*;
using reference = pair&;
using const_reference = const pair&;
using difference_type = ptrdiff_t;
using size_type = size_t;
private:
struct BinaryNode
{
pair element;
BinaryNode *left;
BinaryNode *right;
BinaryNode( const_reference theElement, BinaryNode *lt, BinaryNode *rt )
: element{ theElement }, left{ lt }, right{ rt } { }
BinaryNode( pair && theElement, BinaryNode *lt, BinaryNode *rt )
: element{ std::move( theElement ) }, left{ lt }, right{ rt } { }
};
using node = BinaryNode;
using node_ptr = node*;
using const_node_ptr = const node*;
node_ptr _root;
size_type _size;
key_compare comp;
public:
BinarySearchTree() {
// TODO
}
BinarySearchTree( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree( BinarySearchTree && rhs ) {
// TODO
}
~BinarySearchTree() {
// TODO
}
const_reference min() const { return min( _root )->element; }
const_reference max() const { return max( _root )->element; }
const_reference root() const {
// TODO
}
bool contains( const key_type & x ) const { return contains( x, _root ); }
value_type & find( const key_type & key ) { return find( key, _root )->element.second; }
const value_type & find( const key_type & key ) const { return find( key, _root )-
>element.second; }
bool empty() const {
// TODO
}
size_type size() const {
// TODO
}
void clear() {
clear( _root );
_size = 0;
}
void insert( const_reference x ) { insert( x, _root ); }
void insert( pair && x ) { insert( std::move( x ), _root ); }
void erase( const key_type & x ) { erase(x, _root); }
BinarySearchTree & operator=( const BinarySearchTree & rhs ) {
// TODO
}
BinarySearchTree & operator=( BinarySearchTree && rhs ) {
// TODO
}
private:
void insert( const_reference x, node_ptr & t ) {
// TODO
}
void insert( pair && x, node_ptr & t ) {
// TODO
}
void erase( const key_type & x, node_ptr & t ) {
// TODO
}
const_node_ptr min( const_node_ptr t ) const {
// TODO
}
const_node_ptr max( const_node_ptr t ) const {
// TODO
}
bool contains( const key_type & x, const_node_ptr t ) const {
// TODO
}
node_ptr find( const key_type & key, node_ptr t ) {
// TODO
}
const_node_ptr find( const key_type & key, const_node_ptr t ) const {
// TODO
}
void clear( node_ptr & t ) {
// TODO
}
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// TODO
}
public:
template <typename KK, typename VV, typename CC>
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import numpy as np
import matplotlib.pyplot as plt
import keras
from keras.layers import Input, Dense, Reshape, Flatten, Dropout
from keras.layers import BatchNormalization, Activation, ZeroPadding2D
from keras.layers import LeakyReLU
from keras.layers.convolutional import UpSampling2D, Conv2D
from keras.models import Sequential, Model
from keras.optimizers import Adam,SGD
from keras.datasets import cifar10
#Step 2: Load the data.
#Loading the CIFAR10 data
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#Selecting a single class of images
#The number was randomly chosen and any number
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model.add(BatchNormalization(momentum=0.78))
model.add(Activation("relu"))
model.add(UpSampling2D())
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model.add(BatchNormalization(momentum=0.78))
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def build_discriminator():
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model.add(LeakyReLU(alpha=0.25))
model.add(Dropout(0.25))
#Building the output layer
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
image = Input(shape=image_shape)
validity = model(image)
return Model(image, validity)
#Step 6: Define a utility function to display the generated images.
def display_images():
r, c = 4,4
noise = np.random.normal(0, 1, (r * c,latent_dimensions))
generated_images = generator.predict(noise)
#Scaling the generated images
generated_images = 0.5 * genera.
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#include<math.h>
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struct tugboat {
int x, y, dist;
char name[21];
};
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scanf("%d%d%d%d", &xs, &ys, &xn, &yn);
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qsort(tugboats, n, sizeof(struct tugboat), cmp);
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int prev_x = xs, prev_y = ys;
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num_tugs++;
strcpy(tugs[num_tugs - 1], tugboats[i].name);
prev_x = tugboats[i].x;
prev_y = tugboats[i].y;
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}
printf("%d\n", num_tugs);
for (i = 0; i < num_tugs; i++) {
printf("%s\n", tugs[i]);
}
return 0;
}
Here are the instructions:
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KKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK
Biological Screening of Herbal Drugs in detailed.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Introduction to AI for Nonprofits with Tapp Network
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
The geography of Taylor Swift - some ideas
Geographical themes connected with Taylor Swift's ERAS tour - coming to the UK in June 2024
The approach at University of Liverpool.pptx
How libraries can support authors with open access requirements for UKRI funded books
Wednesday 22 May 2024, 14:00-15:00.
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...
BÀI TẬP BỔ TRỢ TIẾNG ANH GLOBAL SUCCESS LỚP 3 - CẢ NĂM (CÓ FILE NGHE VÀ ĐÁP Á...Nguyen Thanh Tu Collection
https://app.box.com/s/hqnndn05v4q5a4k4jd597rkdbda0fniiHow to Make a Field invisible in Odoo 17
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
Unit 8 - Information and Communication Technology (Paper I).pdf
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
Thesis Statement for students diagnonsed withADHD.ppt
Presentation required for the master in Education.
Instructions for Submissions thorugh G- Classroom.pptx
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
The Challenger.pdf DNHS Official Publication
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
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Introduction to AI for Nonprofits with Tapp Network
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